The status and challenge of global fire modelling
Stijn Hantson,Almut Arneth,Sandy P. Harrison,Sandy P. Harrison,Douglas I. Kelley,Douglas I. Kelley,I. Colin Prentice,I. Colin Prentice,Sam Rabin,Sally Archibald,Sally Archibald,Florent Mouillot,Steve R. Arnold,Paulo Artaxo,Dominique Bachelet,Philippe Ciais,Matthew Forrest,Pierre Friedlingstein,Thomas Hickler,Jed O. Kaplan,Silvia Kloster,Wolfgang Knorr,Gitta Lasslop,Fang Li,Stéphane Mangeon,Joe R. Melton,Andrea Meyn,Stephen Sitch,Allan Spessa,Allan Spessa,Guido R. van der Werf,Apostolos Voulgarakis,Chao Yue +32 more
TLDR
The Fire Model Intercomparison Project (FireMIP) is an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions as discussed by the authors.Abstract:
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.read more
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Deep learning and process understanding for data-driven Earth system science
Markus Reichstein,Gustau Camps-Valls,Bjorn Stevens,Martin Jung,Joachim Denzler,Nuno Carvalhais,Nuno Carvalhais,Prabhat +7 more
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
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Global fire emissions estimates during 1997–2016
Guido R. van der Werf,James T. Randerson,Louis Giglio,Thijs T. van Leeuwen,Yang Chen,Brendan M. Rogers,Mingquan Mu,Margreet J. E. van Marle,Douglas C. Morton,G. James Collatz,Robert J. Yokelson,Prasad S. Kasibhatla +11 more
TL;DR: The Global Fire Emissions Database (GFED) as mentioned in this paper has been used to quantify global fire emissions patterns during 1997-2016, with the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia.
Journal ArticleDOI
A human-driven decline in global burned area
Niels Andela,Niels Andela,Douglas C. Morton,Louis Giglio,Yang Chen,G. R. van der Werf,Prasad S. Kasibhatla,Ruth DeFries,G. J. Collatz,Stijn Hantson,Silvia Kloster,Dominique Bachelet,Matthew Forrest,Gitta Lasslop,Fang Li,Stéphane Mangeon,Joe R. Melton,Chao Yue,James T. Randerson +18 more
TL;DR: Assessing long-term fire trends using multiple satellite data sets found that global burned area declined by 24.3 ± 8.8% over the past 18 years, and the estimated decrease in burned area remained robust after adjusting for precipitation variability and was largest in savannas.
Journal ArticleDOI
Vegetation demographics in Earth System Models: A review of progress and priorities.
Rosie A. Fisher,Charles D. Koven,William R. L. Anderegg,Bradley O. Christoffersen,Michael Dietze,Caroline E. Farrior,Jennifer A. Holm,George C. Hurtt,Ryan G. Knox,Peter Lawrence,Jeremy W. Lichstein,Marcos Longo,Ashley M. Matheny,David Medvigy,Helene C. Muller-Landau,Thomas L. Powell,Shawn P. Serbin,Hisashi Sato,Jacquelyn K. Shuman,Benjamin Smith,Anna T. Trugman,Toni Viskari,Hans Verbeeck,Ensheng Weng,Chonggang Xu,Xiangtao Xu,Tao Zhang,Paul R. Moorcroft +27 more
TL;DR: It is argued that stronger and more innovative connections to data are required to address gaps in understanding, and that constrained predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.
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Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models
Gordon B. Bonan,Scott C. Doney +1 more
TL;DR: Environmental changes that are stressing terrestrial and marine ecosystems are described and how these stressors are being included in ESMs are discussed, initially with an emphasis on climate processes, but also show their emerging utility for VIA analyses and examine them in the context of Earth system prediction.
References
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Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model
Stephen Sitch,Benjamin Smith,Iain Colin Prentice,Almut Arneth,Alberte Bondeau,Wolfgang Cramer,Jed O. Kaplan,Samuel Levis,Samuel Levis,Wolfgang Lucht,Martin T. Sykes,Kirsten Thonicke,Sergey Venevsky +12 more
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TL;DR: What is known and what is needed to develop a holistic understanding of the role of fire in the Earth system are reviewed, particularly in view of the pervasive impact of fires and the likelihood that they will become increasingly difficult to control as climate changes.
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Carbon and Other Biogeochemical Cycles
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Book
A Mathematical Model for Predicting Fire Spread in Wildland Fuels
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